NauSim Un simulador de código abierto para el control, desarrollo y despliegue de drones submarinos
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- César Antonio Ortiz Toro+−
- Cristina Cerrada Collado+−
- David Moreno Salinas+−
- Dictino Chaos García +−
- Karen Lyn García Suárez +−
- Pablo Otero Roth+−
- Juan Manuel Vidal Pérez +−
- Miguel Ángel Luque Nieto+−
- Ana Isabel Vázquez +−
- José Jesús Fraile Ardanuy+−
- Vicente Negro Valdecantos+−
- Eugenio Jiménez Yguacel +−
- Joaquín Aranda Almansa+−
- Santiago Zazo Bello+−
- Pedro José Zufiria Zatarain +−
- Luis Magdalena Layos+−
- Juan Parras Moral+−
- Alvaro Gutiérrez Martín+−
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Amer, A., Álvarez-Tuñón, O., Ugurlu, H. I., Sejersen, J. L. F., Brodskiy, Y., Kayacan, E., 2023. Unav-sim: A visually realistic underwater robotics simulator and synthetic data-generation framework. In: 2023 21st International Conference on Advanced Robotics (ICAR). IEEE, pp. 570–576. DOI: 10.48550/arXiv.2310.11927 DOI: https://doi.org/10.1109/ICAR58858.2023.10406819
Betancourt, J., Coral, W., Colorado, J., 2020. An integrated rov solution for underwater net-cage inspection in fish farms using computer vision. SN Applied Sciences 2 (12), 1946. DOI: 10.1007/s42452-020-03623-z DOI: https://doi.org/10.1007/s42452-020-03623-z
Cerqueira, R., Trocoli, T., Neves, G., Joyeux, S., Albiez, J., Oliveira, L., 2017. A novel gpu-based sonar simulator for real-time applications. Computers & Graphics 68, 66–76. DOI: 10.1016/j.cag.2017.08.008 DOI: https://doi.org/10.1016/j.cag.2017.08.008
Cheng, L., Tan, X., Yao, D., Xu, W., Wu, H., Chen, Y., 2021. A fishery water quality monitoring and prediction evaluation system for floating uav based on time series. Sensors 21 (13), 4451. DOI: 10.3390/s21134451 DOI: https://doi.org/10.3390/s21134451
Coumans, E., 2015. Bullet physics simulation. In: ACM SIGGRAPH 2015 Courses. p. 1. DOI: 10.1145/2776880.2792704 DOI: https://doi.org/10.1145/2776880.2792704
de Cerqueira Gava, P. D., Nascimento Júnior, C. L., Belchior de Franc¸a Silva, J. R., Adabo, G. J., 2022. Simu2vita: A general purpose underwater vehicle simulator. Sensors 22 (9), 3255. DOI: 10.3390/s22093255 DOI: https://doi.org/10.3390/s22093255
Fossen, T. I., 2011. Handbook of marine craft hydrodynamics and motion control. John Wiley & Sons. DOI: 10.1002/9781119994138 DOI: https://doi.org/10.1002/9781119994138
Goslin, M., Mine, M. R., 2004. The panda3d graphics engine. Computer (10), 112–114. DOI: 10.1109/MC.2004.180 DOI: https://doi.org/10.1109/MC.2004.180
Hu, S., Feng, A., Shi, J., Li, J., Khan, F., Zhu, H., Chen, J., Chen, G., 2022. Underwater gas leak detection using an autonomous underwater vehicle (robotic fish). Process Safety and Environmental Protection 167, 89–96. DOI: 10.1016/j.psep.2022.09.002 DOI: https://doi.org/10.1016/j.psep.2022.09.002
Liniger, J., Jensen, A. L., Pedersen, S., Sørensen, H., Mai, C., 2022. On the autonomous inspection and classification of marine growth on subsea structures. In: OCEANS 2022-Chennai. IEEE, pp. 1–7. DOI: 10.1109/OCEANSChennai45887.2022.9775295 DOI: https://doi.org/10.1109/OCEANSChennai45887.2022.9775295
Loncar, I., Obradovic, J., Krasevac, N., Mandic, L., Kvasic, I., Ferreira, F., Slosic, V., Nad, D., Miskovic, N., 2022. Marus-a marine robotics simulator. In: OCEANS 2022, Hampton Roads. IEEE, pp. 1–7. DOI: https://doi.org/10.1109/OCEANS47191.2022.9976969
Madeo, D., Pozzebon, A., Mocenni, C., Bertoni, D., 2020. A low-cost unmanned surface vehicle for pervasive water quality monitoring. IEEE Transactions on Instrumentation and Measurement 69 (4), 1433–1444. DOI: 10.1109/TIM.2019.2963515 DOI: https://doi.org/10.1109/TIM.2019.2963515
Manhaes, M. M. M., Scherer, S. A., Voss, M., Douat, L. R., Rauschenbach, T., 2016. Uuv simulator: A gazebo-based package for underwater intervention and multi-robot simulation. In: OCEANS 2016 MTS/IEEE Monterey. Ieee, pp. 1–8. DOI: 10.1109/OCEANS.2016.7761080 DOI: https://doi.org/10.1109/OCEANS.2016.7761080
Potokar, E., Ashford, S., Kaess, M., Mangelson, J. G., 2022. Holoocean: An underwater robotics simulator. In: 2022 International Conference on Robotics and Automation (ICRA). IEEE, pp. 3040–3046. DOI: 10.1109/ICRA46639.2022.9812353 DOI: https://doi.org/10.1109/ICRA46639.2022.9812353
Prats, M., Perez, J., Fern´andez, J. J., Sanz, P. J., 2012. An open source tool for simulation and supervision of underwater intervention missions. In: IEEE/RSJ international conference on Intelligent Robots and Systems. IEEE, pp. 2577–2582. DOI: 10.1109/IROS.2012.6385788 DOI: https://doi.org/10.1109/IROS.2012.6385788
Raschka, S., Patterson, J., Nolet, C., 2020. Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information 11 (4), 193. DOI: 10.3390/info11040193 DOI: https://doi.org/10.3390/info11040193
Robotics, B., 2016. Bluerov2: The world’s most affordable high-performance rov. BlueROV2 Datasheet; Blue Robotics: Torrance, CA, USA.
Rofallski, R., Tholen, C., Helmholz, P., Parnum, I., Luhmann, T., 2020. Measuring artificial reefs using a multi-camera system for unmanned underwater vehicles. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 43 (B2), 999–1008. DOI: 10.5194/isprs-archives-XLIII-B2-2020-999-2020 DOI: https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-999-2020
Smith, R., et al., 2005. Open dynamics engine.
Sultonov, S., 2023. Importance of python programming language in machine learning. International Bulletin of Engineering and Technology 3 (9), –30.
von Benzon, M., Sørensen, F. F., Uth, E., Jouffroy, J., Liniger, J., Pedersen, S., 2022. An open-source benchmark simulator: Control of a bluerov2 underwater robot. Journal of Marine Science and Engineering 10 (12), 1898. DOI: 10.3390/jmse10121898 DOI: https://doi.org/10.3390/jmse10121898
Wu, C.-J., 2018. 6-dof modelling and control of a remotely operated vehicle. Ph.D. thesis.